Model Card: Math Homework Solver

Model Details

• Base Model: Llama 3.1 (8 Billion parameters)
• Fine-tuning Method: PEFT (Parameter-Efficient Fine-Tuning) with QLoRA
• Quantization: 4-bit quantization for reduced memory usage
• Training Framework: Unsloth, optimized for efficient fine-tuning of large language models
• Training Environment: Google Colab (free tier), NVIDIA T4 GPU (16GB VRAM), 12GB RAM
• Dataset Used: Combination of ambrosfitz/10k_history_data_v4, adamo1139/basic_economics_questions_ts_test_1, adamo1139/basic_economics_questions_ts_test_2, adamo1139/basic_economics_questions_ts_test_3, adamo1139/basic_economics_questions_ts_test_4
• Git Repo: The git repo on my github account is justsomerandomdude264/Homework_Solver_LLM

Capabilities

Getting Started

1. Clone the repo
   git clone https://huggingface.co/justsomerandomdude264/SocialScience_Homework_Solver-Llama3.18B
2. Run inference
   1. This method is recommended as its reliable and accurate:
   python

   1from unsloth import FastLanguageModel 2import torch 3 4# Define Your Question 5question = "Analyze the socio-political and economic factors that contributed to the rise and fall of the Byzantine Empire from the reign of Justinian I to the fall of Constantinople in 1453. How did internal conflicts, religious controversies, and external pressures from both Islamic caliphates and Western European powers shape the trajectory of the empire over this period?" # Example Question, You can change it with one of your own 6 7# Load the model 8model, tokenizer = FastLanguageModel.from_pretrained( 9 model_name = "SocialScience_Homework_Solver_Llama318B/model_adapters", # The dir where the repo is cloned or "\\" for root 10 max_seq_length = 2048, 11 dtype = None, 12 load_in_4bit = True, 13 ) 14 15# Set the model in inference model 16FastLanguageModel.for_inference(model) 17 18# QA template 19qa_template = """Question: {} 20Answer: {}""" 21 22# Tokenize inputs 23inputs = tokenizer( 24[ 25 qa_template.format( 26 question, # Question 27 "", # Answer - left blank for generation 28 ) 29], return_tensors = "pt").to("cuda") 30 31# Stream the answer/output of the model 32from transformers import TextStreamer 33text_streamer = TextStreamer(tokenizer) 34_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)
   1. Another way to run inference is to use the merged adapters (not recommend as it gives inaccurate/different answers):
   python

   1from transformers import LlamaForCausalLM, AutoTokenizer 2 3# Load the model 4model = LlamaForCausalLM.from_pretrained( 5 "justsomerandomdude264/SocialScience_Homework_Solver_Llama318B", 6 device_map="auto" 7) 8 9# Load the tokenizer 10tokenizer = AutoTokenizer.from_pretrained("justsomerandomdude264/SocialScience_Homework_Solver_Llama318B") 11 12# Set the inputs up 13qa_template = """Question: {} 14Answer: {}""" 15 16inputs = tokenizer( 17[ 18 qa_template.format( 19 "Who was Akbar?", # Question 20 "", # output - leave this blank for generation! 21 ) 22], return_tensors = "pt").to("cuda") 23 24# Do a forward pass 25outputs = model.generate(**inputs, max_new_tokens = 128, use_cache = True) 26raw_output = str(tokenizer.batch_decode(outputs)) 27 28# Formtting the string 29# Removing the list brackets and splitting the string by newline characters 30formatted_string = raw_output.strip("[]").replace("<|begin_of_text|>", "").replace("<|eot_id|>", "").strip("''").split("\\n") 31 32# Print the lines one by one 33for line in formatted_string: 34 print(line)

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